Automated text classification of opinion vs. news French press articles. A comparison of transformer and feature-based approaches
(2024) Language & Communication — Vol. 99, n° 1, p. 129-140 (2024)
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- Abstract
- This study explores Natural Language Processing (NLP) methods for distinguishing between press articles belonging to the journalistic genres of ‘objective’ news and ‘subjective’ opinion. Two classification models are compared: CamemBERT, a French transformer model fine-tuned for the task, and a machine learning model using 32 linguistic features. Trained on 8000 Belgian French articles, both models are evaluated on 1000 Canadian French articles. Results show CamemBERT’s superiority but highlight potential for hybrid approaches and emphasizes the need for robust and transparent methods in NLP. The research contributes to understanding NLP’s role in journalism by addressing challenges of point of view detection in press discourse.
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Citations
Escouflaire, L., Descampe, A., & Fairon, C. (2024). Automated text classification of opinion vs. news French press articles. A comparison of transformer and feature-based approaches. Language & Communication, 99(1), 129-140. https://doi.org/10.1016/j.langcom.2024.09.004 (Original work published 2024)